OpenRaiser/Intern-Atlas huggingface dataset:
Design, Construction, and Use of a Single Board Computer Beowulf Cluster: Application of the Small-Footprint, Low-Cost, InSignal 5420 Octa Board
Venue: arXiv cs.DC 2015
Year: 2015

In recent years development in the area of Single Board Computing has been advancing rapidly. At Wolters Kluwer's Corporate Legal Services Division a prototyping effort was undertaken to establish the utility of such devices for practical and general computing needs. This paper presents the background of this work, the design and construction of a 64 core 96 GHz cluster, and their possibility of yielding approximately 400 GFLOPs from a set of small footprint InSignal boards created for just over $2,300. Additionally this paper discusses the software environment on the cluster, the use of a standard Beowulf library and its operation, as well as other software application uses including Elastic Search and ownCloud. Finally, consideration will be given to the future use of such technologies in a business setting in order to introduce new Open Source technologies, reduce computing costs, and improve Time to Market. Index Terms: Single Board Computing, Raspberry Pi, InSignal Exynos 5420, Linaro Ubuntu Linux, High Performance Computing, Beowulf clustering, Open Source, MySQL, MongoDB, ownCloud, Computing Architectures, Parallel Computing, Cluster Computing
Speculative Approximations for Terascale Analytics
Venue: arXiv cs.DB 2015
Year: 2015

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the incapacity to evaluate multiple parameter configurations simultaneously and the lack of support to quickly identify sub-optimal configurations are the principal causes. In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced parallel multi-query processing methods to evaluate several configurations concurrently. The number of configurations is determined adaptively at runtime, while the configurations themselves are extracted from a distribution that is continuously learned following a Bayesian process. Online aggregation is applied to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. We apply the proposed techniques to distributed gradient descent optimization -- batch and incremental -- for support vector machines and logistic regression models. We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big Data analytics system -- and evaluate their performance over terascale-size synthetic and real datasets. The results confirm that as many as 32 configurations can be evaluated concurrently almost as fast as one, while sub-optimal configurations are detected accurately in as little as a $1/20^{\text{th}}$ fraction of the time.
Fast Generation of Random Spanning Trees and the Effective Resistance Metric
Venue: arXiv cs.DS 2015
Year: 2015

We present a new algorithm for generating a uniformly random spanning tree in an undirected graph. Our algorithm samples such a tree in expected $\tilde{O}(m^{4/3})$ time. This improves over the best previously known bound of $\min(\tilde{O}(m\sqrt{n}),O(n^ω))$ -- that follows from the work of Kelner and Mądry [FOCS'09] and of Colbourn et al. [J. Algorithms'96] -- whenever the input graph is sufficiently sparse. At a high level, our result stems from carefully exploiting the interplay of random spanning trees, random walks, and the notion of effective resistance, as well as from devising a way to algorithmically relate these concepts to the combinatorial structure of the graph. This involves, in particular, establishing a new connection between the effective resistance metric and the cut structure of the underlying graph.
Sequence Modeling using Gated Recurrent Neural Networks
Venue: arXiv cs.NE 2015
Year: 2015

In this paper, we have used Recurrent Neural Networks to capture and model human motion data and generate motions by prediction of the next immediate data point at each time-step. Our RNN is armed with recently proposed Gated Recurrent Units which has shown promising results in some sequence modeling problems such as Machine Translation and Speech Synthesis. We demonstrate that this model is able to capture long-term dependencies in data and generate realistic motions.
Multi-Access Communications with Energy Harvesting: A Multi-Armed Bandit Model and the Optimality of the Myopic Policy
Venue: arXiv cs.IT 2015
Year: 2015

A multi-access wireless network with N transmitting nodes, each equipped with an energy harvesting (EH) device and a rechargeable battery of finite capacity, is studied. At each time slot (TS) a node is operative with a certain probability, which may depend on the availability of data, or the state of its channel. The energy arrival process at each node is modelled as an independent two-state Markov process, such that, at each TS, a node either harvests one unit of energy, or none. At each TS a subset of the nodes is scheduled by the access point (AP). The scheduling policy that maximises the total throughput is studied assuming that the AP does not know the states of either the EH processes or the batteries. The problem is identified as a restless multiarmed bandit (RMAB) problem, and an upper bound on the optimal scheduling policy is found. Under certain assumptions regarding the EH processes and the battery sizes, the optimality of the myopic policy (MP) is proven. For the general case, the performance of MP is compared numerically to the upper bound.
Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we propose fusion of dynamic TOA (time of arrival) from multiple non-coherent detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We also show that by using multiple of these energy detectors, we can achieve the performance of a digital matched filter implementation in the AWGN (additive white Gaussian noise) setting. We derive analytical expression for number of energy detectors needed to achieve the matched filter performance. We demonstrate in simulation the validity of our analytical approach. Results indicate that number of energy detectors needed will be high at low SNRs and converge to a constant number as the SNR increases. We also study the performance of the strategy proposed using IEEE 802.15.4a CM1 channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter.
Can Fires, Night Lights, and Mobile Phones reveal behavioral fingerprints useful for Development?
Venue: arXiv cs.CY 2015
Year: 2015

Fires, lights at night and mobile phone activity have been separately used as proxy indicators of human activity with high potential for measuring human development. In this preliminary report, we develop some tools and methodologies to identify and visualize relations among remote sensing datasets containing fires and night lights information with mobile phone activity in Cote D'Ivoire from December 2011 to April 2012.
A Transfer Theorem for the Separation Problem
Venue: arXiv cs.FL 2015
Year: 2015

We investigate two problems for a class C of regular word languages. The C-membership problem asks for an algorithm to decide whether an input language belongs to C. The C-separation problem asks for an algorithm that, given as input two regular languages, decides whether there exists a third language in C containing the first language, while being disjoint from the second. These problems are considered as means to obtain a deep understanding of the class C. It is usual for such classes to be defined by logical formalisms. Logics are often built on top of each other, by adding new predicates. A natural construction is to enrich a logic with the successor relation. In this paper, we obtain simple self-contained proofs of two transfer results: we show that for suitable logically defined classes, the membership, resp. the separation problem for a class enriched with the successor relation reduces to the same problem for the original class. Our reductions work both for languages of finite words and infinite words. The proofs are mostly self-contained, and only require a basic background on regular languages. This paper therefore gives new, simple proofs of results that were considered as difficult, such as the decid- ability of the membership problem for the levels 1, 3/2, 2 and 5/2 of the dot-depth hierarchy.
Parking Space Management via Dynamic Performance-Based Pricing
Venue: arXiv cs.GT 2015
Year: 2015

In congested urban areas, it remains a pressing challenge to reduce unnecessary vehicle circling for parking while at the same time maximize parking space utilization. In observance of new information technologies that have become readily accessible to drivers and parking agencies, we develop a dynamic non-cooperative bi-level model (i.e. Stackelberg leader-follower game) to set parking prices in real-time for effective parking access and space utilization. The model is expected to fit into an integrated parking pricing and management system, where parking reservations and transactions are facilitated by sensing and informatics infrastructures, that ensures the availability of convenient spaces at equilibrium market prices. It is shown with numerical examples that the proposed dynamic parking pricing model has the potential to virtually eliminate vehicle circling for parking, which results in significant reduction in adverse socioeconomic externalities such as traffic congestion and emissions.
Hostile Intent Identification by Movement Pattern Analysis: Using Artificial Neural Networks
Venue: arXiv cs.AI 2015
Year: 2015

In the recent years, the problem of identifying suspicious behavior has gained importance and identifying this behavior using computational systems and autonomous algorithms is highly desirable in a tactical scenario. So far, the solutions have been primarily manual which elicit human observation of entities to discern the hostility of the situation. To cater to this problem statement, a number of fully automated and partially automated solutions exist. But, these solutions lack the capability of learning from experiences and work in conjunction with human supervision which is extremely prone to error. In this paper, a generalized methodology to predict the hostility of a given object based on its movement patterns is proposed which has the ability to learn and is based upon the mechanism of humans of learning from experiences. The methodology so proposed has been implemented in a computer simulation. The results show that the posited methodology has the potential to be applied in real world tactical scenarios.
Using Object-Relational Mapping to Create the Distributed Databases in a Hybrid Cloud Infrastructure
Venue: arXiv cs.DB 2015
Year: 2015

One of the challenges currently problems in the use of cloud services is the task of designing of specialized data management systems. This is especially important for hybrid systems in which the data are located in public and private clouds. Implementation monitoring functions querying, scheduling and processing software must be properly implemented and is an integral part of the system. To provide these functions is proposed to use an object-relational mapping (ORM). The article devoted to presenting the approach of designing databases for information systems hosted in a hybrid cloud infrastructure. It also provides an example of the development of ORM library.
Performance Analysis of Simultaneous Wireless Information and Power Transfer with Ambient RF Energy Harvesting
Venue: arXiv cs.IT 2015
Year: 2015

The advance in RF energy transfer and harvesting technique over the past decade has enabled wireless energy replenishment for electronic devices, which is deemed as a promising alternative to address the energy bottleneck of conventional battery-powered devices. In this paper, by using a stochastic geometry approach, we aim to analyze the performance of an RF-powered wireless sensor in a downlink simultaneous wireless information and power transfer (SWIPT) system with ambient RF transmitters. Specifically, we consider the point-to-point downlink SWIPT transmission from an access point to a wireless sensor in a network, where ambient RF transmitters are distributed as a Ginibre ?$α$-determinantal point process (DPP), which becomes the Poisson point process when $α$? approaches zero. In the considered network, we focus on analyzing the performance of a sensor equipped with the power-splitting architecture. Under this architecture, we characterize the expected RF energy harvesting rate of the sensor. Moreover, we derive the upper bound of both power and transmission outage probabilities. Numerical results show that our upper bounds are accurate for different value of ?$α$.
Concept-oriented programming: from classes to concepts and from inheritance to inclusion
Venue: arXiv cs.PL 2015
Year: 2015

For the past several decades, programmers have been modeling things in the world with trees using hierarchies of classes and object-oriented programming (OOP) languages. In this paper, we describe a novel approach to programming, called concept-oriented programming (COP), which generalizes classes and inheritance by introducing concepts and inclusion, respectively.
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
Venue: arXiv cs.CV 2015
Year: 2015

In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering.
Group $K$-Means
Venue: arXiv cs.CV 2015
Year: 2015

We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary. Although theoretically low approximation errors can be achieved by the global solution, an effective solution has not been well studied in practice. To solve the problem, we propose a simple yet effective algorithm \textit{Group $K$-Means}. Specifically, we take each dictionary, or any two selected dictionaries, as a group of $K$-means cluster centers, and then deal with the approximation issue by minimizing the approximation errors. Besides, we propose a hierarchical initialization for such a non-convex problem. Experimental results well validate the effectiveness of the approach.
Online Reputation and Polling Systems: Data Incest, Social Learning and Revealed Preferences
Venue: arXiv cs.SI 2015
Year: 2015

This paper considers online reputation and polling systems where individuals make recommendations based on their private observations and recommendations of friends. Such interaction of individuals and their social influence is modelled as social learning on a directed acyclic graph. Data incest (misinformation propagation) occurs due to unintentional re-use of identical actions in the for- mation of public belief in social learning; the information gathered by each agent is mistakenly considered to be independent. This results in overconfidence and bias in estimates of the state. Necessary and sufficient conditions are given on the structure of information exchange graph to mitigate data incest. Incest removal algorithms are presented. Experimental results on human subjects are presented to illustrate the effect of social influence and data incest on decision making. These experimental results indicate that social learning protocols require careful design to handle and mitigate data incest. The incest removal algorithms are illustrated in an expectation polling system where participants in a poll respond with a summary of their friends' beliefs. Finally, the principle of revealed preferences arising in micro-economics theory is used to parse Twitter datasets to determine if social sensors are utility maximizers and then determine their utility functions.
Optimal Data-Dependent Hashing for Approximate Near Neighbors
Venue: arXiv cs.DS 2015
Year: 2015

We show an optimal data-dependent hashing scheme for the approximate near neighbor problem. For an $n$-point data set in a $d$-dimensional space our data structure achieves query time $O(d n^{ρ+o(1)})$ and space $O(n^{1+ρ+o(1)} + dn)$, where $ρ=\tfrac{1}{2c^2-1}$ for the Euclidean space and approximation $c>1$. For the Hamming space, we obtain an exponent of $ρ=\tfrac{1}{2c-1}$. Our result completes the direction set forth in [AINR14] who gave a proof-of-concept that data-dependent hashing can outperform classical Locality Sensitive Hashing (LSH). In contrast to [AINR14], the new bound is not only optimal, but in fact improves over the best (optimal) LSH data structures [IM98,AI06] for all approximation factors $c>1$. From the technical perspective, we proceed by decomposing an arbitrary dataset into several subsets that are, in a certain sense, pseudo-random.
Elastic Processing of Analytical Query Workloads on IaaS Clouds
Venue: arXiv cs.DB 2015
Year: 2015

Many modern applications require the evaluation of analytical queries on large amounts of data. Such queries entail joins and heavy aggregations that often include user-defined functions (UDFs). The most efficient way to process these specific type of queries is using tree execution plans. In this work, we develop an engine for analytical query processing and a suite of specialized techniques that collectively take advantage of the tree form of such plans. The engine executes these tree plans in an elastic IaaS cloud infrastructure and dynamically adapts by allocating and releasing pertinent resources based on the query workload monitored over a sliding time window. The engine offers its services for a fee according to service-level agreements (SLAs) associated with the incoming queries; its management of cloud resources aims at maximizing the profit after removing the costs of using these resources. We have fully implemented our algorithms in the Exareme dataflow processing system. We present an extensive evaluation that demonstrates that our approach is very efficient (exhibiting fast response times), elastic (successfully adjusting the cloud resources it uses as the engine continually adapts to query workload changes), and profitable (approximating very well the maximum difference between SLA-based income and cloud-based expenses).
PC Guided Automatic Vehicle System
Venue: arXiv cs.OH 2015
Year: 2015

The main objective of this paper is to design and develop an automatic vehicle, fully controlled by a computer system. The vehicle designed in the present work can move in a pre-determined path and work automatically without the need of any human operator and it also controlled by human operator. Such a vehicle is capable of performing wide variety of difficult tasks in space research, domestic, scientific and industrial fields. For this purpose, an IBM compatible PC with Pentium microprocessor has been used which performed the function of the system controller. Its parallel printer port has been used as data communication port to interface the vehicle. A suitable software program has been developed for the system controller to send commands to the vehicle.
*-Continuous Kleene $ω$-Algebras
Venue: arXiv cs.FL 2015
Year: 2015

We define and study basic properties of *-continuous Kleene $ω$-algebras that involve a *-continuous Kleene algebra with a *-continuous action on a semimodule and an infinite product operation that is also *-continuous. We show that *-continuous Kleene $ω$-algebras give rise to iteration semiring-semimodule pairs. We show how our work can be applied to solve certain energy problems for hybrid systems.
Recovering Intuition from Automated Formal Proofs using Tableaux with Superdeduction
Venue: arXiv cs.LO 2015
Year: 2015

We propose an automated deduction method which allows us to produce proofs close to the human intuition and practice. This method is based on tableaux, which generate more natural proofs than similar methods relying on clausal forms, and uses the principles of superdeduction, among which the theory is used to enrich the deduction system with new deduction rules. We present two implementations of this method, which consist of extensions of the Zenon automated theorem prover. The first implementation is a version dedicated to the set theory of the B formal method, while the second implementation is a generic version able to deal with any first order theory. We also provide several examples of problems, which can be handled by these tools and which come from different theories, such as the B set theory or theories of the TPTP library.
Iterative network-channel decoding with cooperative space-time transmission
Venue: arXiv cs.IT 2015
Year: 2015

One of the most efficient methods of exploiting space diversity for portable wireless devices is cooperative communication utilizing space-time block codes. In cooperative communication, users besides communicating their own information, also relay the information of other users. In this paper we investigate a scheme where cooperation is achieved using two methods, namely, distributed space-time coding and network coding. Two cooperating users utilize Alamouti space time code for inter-user cooperation and in addition utilize a third relay which performs network coding. The third relay does not have any of its information to be sent. In this paper we propose a scheme utilizing convolutional code based network coding, instead of conventional XOR based network code and utilize iterative joint network-channel decoder for efficient decoding. Extrinsic information transfer (EXIT) chart analysis is performed to investigate the convergence property of the proposed decoder.
Using Socrative and Smartphones for the support of collaborative learning
Venue: arXiv cs.CY 2015
Year: 2015

The integration of new technologies in the classrooms opens new possibilities for the teaching and learning process. Technologies such as student response system (e.g. Clicker) are getting popularity among teachers due to its effects on student learning performance. In this study, our primary objective is to investigate the effect of Socrative with combination of smartphones on student learning performance. We also observed the benefits of interactivity between the teacher and the students and among classmates, which positively influences collaborative learning and engagement of students in the class. We test these relationships experimentally in a community college class environment using data from a survey answered by students in information technology associate degree. The results of our study reveal that collaborative learning and engagement of student in the class improves student learning performance. We highly recommend these tools in educational settings to support the learning process.